Wireless federated learning (FL) relies on efficient uplink communications to aggregate model updates across distributed edge devices. Over-the-air computation (a.k.a. AirComp) has emerged as a promising approach for addressing the scalability challenge of FL over wireless links with limited communication resources. Unlike conventional methods, AirComp allows multiple edge devices to transmit uplink signals simultaneously, enabling the parameter server to directly decode the average global model. However, existing AirComp solutions are intrinsically analog, while modern wireless systems predominantly adopt digital modulations. Consequently, careful constellation designs are necessary to accurately decode the sum model updates without ambiguity. In this paper, we propose an end-to-end communication system supporting AirComp with digital modulation, aiming to overcome the challenges associated with accurate decoding of the sum signal with constellation designs. We leverage autoencoder network structures and explore the joint optimization of transmitter and receiver components. Our approach fills an important gap in the context of accurately decoding the sum signal in digital modulation-based AirComp, which can advance the deployment of FL in contemporary wireless systems.

翻译：无线联邦学习依赖于高效的上行通信来聚合分布式边缘设备的模型更新。空中计算（即AirComp）已成为解决通信资源受限无线链路中联邦学习可扩展性挑战的一种有前景的方法。与传统方法不同，AirComp允许多个边缘设备同时传输上行信号，使参数服务器能够直接解码平均全局模型。然而，现有的AirComp方案本质上是模拟的，而现代无线系统主要采用数字调制。因此，需要精心设计星座图，以准确无歧义地解码求和模型更新。在本文中，我们提出了一种支持数字调制AirComp的端到端通信系统，旨在克服通过星座设计准确解码求和信号所面临的挑战。我们利用自编码器网络结构，探索发射机和接收机组件的联合优化。我们的方法填补了基于数字调制的AirComp中准确解码求和信号这一重要空白，这有助于推动联邦学习在现代无线系统中的部署。